Relation Predictions in Comorbid Disease Centric Knowledge Graph Using Heterogeneous GNN Models

Disease comorbidity has been an important topic of research for the last decade. This topic has become more popular due to the recent outbreak of COVID-19 disease. A comorbid condition due to multiple concurrent diseases is more fatal than a single disease. These comorbid conditions can be caused due to different genetic as well as drug-related side effects on an individual. There are already successful methods for predicting comorbid disease associations. This disease-associated genetic or drug-invasive information can help infer more target factors that cause common diseases. This may further help find out effective drugs for treating a pair of concurrent diseases. In addition to that, the common drug side-effects causing a disease phenotype and the gene associated with that can be helpful in finding important biomarkers for further prognosis of the comorbid disease. In this paper, we use the knowledge graph (KG) from our previous study to find out target-specific relations apart from sole disease-disease associations. We use four different heterogeneous graph neural network models to perform link prediction among different entities in the knowledge graph and we perform a comparative analysis among them. It is found that our best heterogeneous GNN model outperforms existing state-of-the-art models on a few target-specific relationships. Further, we also predict a few novel drug-disease, drug-phenotype, disease-phenotype, and gene-phenotype associations. These interrelated associations are further used to find out the common phenotypes associated with a comorbid disease as well as caused by the direct side effects of a treating drug. In this regard, our methodology also predicts some novel biomarkers and therapeutics for different fatal prevalent diseases.